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A multiobjective genetic algorithm based hybrid recommendation approach

机译：基于多目标遗传算法的混合推荐方法

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Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.
机译：多年来，个性化推荐方法受到了广泛关注。在本文中，我们提出了一种混合推荐方法，该方法融合了基于项目的协作过滤，基于用户的协作过滤和矩阵分解方法。该方法同时考虑了推荐的准确性和多样性的两个目标。首先，通过三种方法分别创建一组项目。然后，将通过三种方法生成的项目组合为一组候选项目。最后，采用多目标遗传算法从集合中选择一组帕累托推荐列表。实验结果表明，与比较方法相比，该方法非常有效，并且能够产生更好的Pareto解。

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